Reliable operation of smart vertical farms requires coordinated sensing, actuation, communication, and control, yet sensor and actuator faults can compromise environmental regulation. This structured narrative review examined signal-processing methods for operational monitoring, anomaly detection, and fault isolation in vertical farms and related controlled-environment systems. The PRISMA-style diagram reports 410 identified records and 78 cited sources. A four-tier framework distinguished direct vertical-farm evidence, adjacent controlled-environment studies, transferable engineering research, and foundational methods. Statistical monitoring and change detection were suitable for slowly varying environmental and nutrient measurements, whereas frequency and time-frequency analyses were more appropriate for electrical, vibration, and acoustic signals. Sensor redundancy, correlation analysis, and model-based residuals supported fault isolation by comparing actuator commands with measured responses. Available evidence was stronger for hydroponic sensor faults than for progressive actuator degradation in commercial vertical farms. Crop development, photoperiod, irrigation, and operating-state changes could also produce anomalies without equipment failure. Reliable diagnosis therefore requires synchronized signals, contextual metadata, state-specific baselines, and independent fault confirmation. Evaluation should consider false alarms, detection delay, isolation accuracy, computational demand, and generalization across facilities. Future priorities include benchmark datasets, multimodal diagnosis, edge implementation, digital-twin-supported evaluation, and safe fault-tolerant control under varying crop stages, equipment conditions, and control schedules.

Precision seed metering has evolved from mechanically optimized devices to sensor-based, digitally connected systems. However, research remains fragmented across metering mechanisms, sensing, data processing, communication, and control. This review examined monitoring and closed-loop control throughout the seed-delivery process, from pickup and release to tube passage, furrow placement, and crop emergence. This structured review synthesized 49 primary studies identified through Scopus and the Web of Science Core Collection, with supplementary searching using Google Scholar and literature coverage through 1 September 2026. Optical and photoelectric sensors provide the most direct evidence for seed-passage detection, whereas encoders, pressure, vacuum, motor-current, and vibration measurements primarily indicate machine or actuator conditions. Machine vision provides spatial information and can support independent reference measurements, but requires suitable imaging conditions and computational resources. Capacitive, acoustic, impact-based, and hybrid approaches remain less extensively validated. Reported performance cannot be directly compared across studies due to differences in seed characteristics, mechanisms, operating speeds, event definitions, disturbances, and reference methods. Most studies demonstrate monitoring, alarms, or open-loop optimization rather than complete sensor-to-actuator correction. An engineering framework is therefore proposed to connect measurement targets, evidence maturity, latency, actuator authority, and independent verification. Future research should prioritize standardized testing, disturbance robustness, synchronized multi-row field validation, and measured end-to-end delay. Transparent reporting of correction accuracy, stability, cost, power demand, and interoperability is also required to support deployment and defensible assessment of closed-loop seeding systems.